The process of association and tracking of sensor detections is a key element in providing situational awareness. When the targets in the scenario are dense and exhibit high maneuverability, Multi-Target Tracking (MTT) becomes a challenging task. The conventional techniques to solve such NP-hard combinatorial optimization problem involves multiple complex models and requires tedious tuning of parameters, failing to provide an acceptable performance within the computational constraints. This paper proposes a model free end-to-end approach for airborne target tracking system using sensor measurements, integrating all the key elements of multi target tracking -- association, prediction and filtering using deep learning with memory. The challenging task of association is performed using the Bi-Directional Long short-term memory (LSTM) whereas filtering and prediction are done using LSTM models. The proposed modular blocks can be independently trained and used in multitude of tracking applications including non co-operative (e.g., radar) and co-operative sensors (e.g., AIS, IFF, ADS-B). Such modular blocks also enhances the interpretability of the deep learning application. It is shown that performance of the proposed technique outperforms conventional state of the art technique Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) filter.

Network topology identification (TI) is an essential function for distributed energy resources management systems (DERMS) to organize and operate widespread distributed energy resources (DERs). In this paper, discriminant analysis (DA) is deployed to develop a network TI function that relies only on the measurements available to DERMS. The propounded method is able to identify the network switching configuration, as well as the status of protective devices. Following, to improve the TI resiliency against the interruption of communication channels, a quadratic programming optimization approach is proposed to recover the missing signals. By deploying the propounded data recovery approach and Bayes' theorem together, a benchmark is developed afterward to identify anomalous measurements. This benchmark can make the TI function resilient against cyber-attacks. Having a low computational burden, this approach is fast-track and can be applied in real-time applications. Sensitivity analysis is performed to assess the contribution of different measurements and the impact of the system load type and loading level on the performance of the proposed approach.

Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio test (GLRT), which jointly estimates the class of interest and the adversarial perturbation. We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise under $\ell_{\infty}$ norm-bounded adversarial perturbations, a setting for which a minimax strategy optimizing for the worst-case attack is known. We show that the GLRT approach yields performance competitive with that of the minimax approach under the worst-case attack, and observe that it yields a better robustness-accuracy trade-off under weaker attacks, depending on the values of signal components relative to the attack budget. We also observe that the GLRT defense generalizes naturally to more complex models for which optimal minimax classifiers are not known.

Trump 2020 communications director Tim Murtaugh weighs in on'America's News HQ.' Conservative activist Candace Owens on Sunday leveled harsh criticism against U.S. intelligence agencies for their supposed inability to root out domestic terrorism while simultaneously being able to "take out" terrorists overseas. "We're supposed to believe that our intelligence agencies can track and take out an Iranian terrorist (Soleimani) overnight but they can't manage to get to the root of ANTIFA and black lives matter-- well-funded domestic terrorist cells that have been operating unchecked for YEARS," she tweeted. Iranian Gen. Qasem Soleimani, the head of the Islamic Revolutionary Guard Corps Quds Forces, was killed in a U.S. drone strike in Baghdad, Iraq on Jan. 3. Administration officials said the strike, authorized by President Trump, was conducted to deter imminent attacks on U.S. interests. Owens' comments follow an evening of unrest that came after the president's supporters were purportedly attacked at the so-called Million MAGA March in Washington, D.C. on Saturday. Many were quick to condemn the media's apparent lack of interest in covering the violence directed at supporters of the president.

Organizations continue to embrace the Internet of Things (IoT), the cloud, and mobile technology. This has influenced considerable changes in the threat landscape and created more vulnerability points. Cybercriminals are leveraging these new vulnerability points to develop and launch sophisticated, high-volume, multi-dimensional attacks. Such attacks mean that data is at risk, and organizations must analyze potentially malicious files. Using artificial intelligence software, organizations can process large volumes of threat data and adequately prevent and respond to breaches and hacks.

Vienna is embarking on a diplomatic initiative to draw up an ethical framework for the use of killer robots on the battlefields of the future. Foreign Minister Alexander Schallenberg said similar standards should be adopted as those established for landmines and cluster weapons. "We have to create rules before killer robots reach the battlefield of this Earth," Schallenberg told the German newspaper Welt am Sonntag. He said the Austrian government was planning a conference in Vienna in 2021 "to usher in a process "to initiate a process that will hopefully lead to an international convention on the use of artificial intelligence on battlefields." Read more: Should'killer robots' be banned?

A Madonna and Child painting with a history almost as enigmatic as the Mona Lisa's smile has been identified as an authentic Raphael canvas by Czech company InsightART, which used a robotic X-ray scanner to investigate the artwork. A Madonna and Child painting with a history almost as enigmatic as the Mona Lisa's smile has been identified as an authentic Raphael canvas by Czech company InsightART, which used a robotic X-ray scanner to investigate the artwork. The 500-year-old painting had long been attributed to Raphael, a contemporary of Leonardo Di Vinci and Michelangelo, but doubts about its authenticity occurred during its recent history. The Madonna and Child painting's turbulent backstory encompasses some of Europe's great historical figures, as well as violent fights and lucrative art deals. Commissioned by Pope Leo X, it has hung in the Vatican as well as passing through the hands of the French royal family and Napoleon.

See also the article by Tadavarthi et al in this issue. Evis Sala, MD, PhD, is the professor of oncological imaging at the University of Cambridge, UK and co-leads the Advanced Cancer Imaging Programme and the Integrative Cancer Medicine Programme for the Cancer Research UK Cambridge Centre. Her current research focuses on radiogenomics through multiomics data integration for evaluation of spatial and temporal tumor heterogeneity and on the applications of AI methods for image reconstruction, segmentation, and data integration. Stephan Ursprung, MD, is a 3rd-year PhD student in the department of radiology at the University of Cambridge, UK. His research focuses on the development of AI models for automated segmentation, lesion classification, and treatment response prediction in renal cancer. Dr Ursprung's interests include health information technology, molecular and physiologic imaging, as well as multiomics data integration.

The origin of the word'bias' has never been quite certain. Linguists reckon that the antecedent of bias is the Old French word'biasi' which meant at an angle or oblique. It came to mean'a one-sided tendency of the mind'. In the old English game of bowls, the ball had asymmetrical weight or bias, which made it roll in a curved line. This is how bias came to be the favoured word for having a disproportionate weight in favour of or against an idea or person.

Certainly, Nigerians look forward to more government investments in the development of digital infrastructure across other sections of the country. So, it is imperative for the government to provide necessary funding to expand this centre to other parts of the country. The National Information Technology Development Agency (NITDA) has again achieved another milestone with the launch of the National Centre for Artificial Intelligence and Robotics (NCAIR) as part of its contribution to the successful implementation of the digital economy. Coming at a time when the global economy is rapidly transforming into the new economy driven by creative innovations derived from Science, Technology, Engineering and Mathematics (STEM), the unveiling of this state-of-the-art technology innovation centre can be described as futuristic, in the sense that it is a remarkable demonstration of proactivity on the part of the Ministry of Communications and Digital Economy and NITDA. NCAIR represents government's determination to create a suitable environment for discovering and harnessing the abundant creative ideas of Nigeria's teeming youth population for national development through the promotion of innovative technologies. With the actualisation of this centre, the youth segment of the population would be challenged to channel their creative energies towards preparing solutions that seek to address future problems or challenges across all sectors of the economy.